Ringg, a voice and chat agent platform, now resolves up to 65% of routine customer inquiries without human intervention, the company announced September 23. The platform handles more than 7 million connected calls each month with an average customer satisfaction score of 4.8. By migrating real-time workloads from GPT-4.1 to GPT-5.6 Luna, Ringg cut model costs by roughly 90%.
The shift signals a practical change in enterprise customer service. AI agents are moving from simple chatbots to autonomous systems that complete complex tasks across voice, chat, WhatsApp, and the web. For operations and support leaders managing fragmented systems, the ability to integrate with CRMs, ticketing systems, payment gateways, and scheduling tools in one orchestration layer addresses a persistent pain point.
How the orchestration layer works
Ringg's platform executes actions across connected systems and escalates to a human only when necessary. A knowledge system combines structured filtering with semantic retrieval across datasets, PDFs, CSVs, and business documents. For longer conversations, the system creates a structured summary at approximately 80,000 tokens, preserving key information without resending entire histories. This lets agents maintain context through multi-step workflows.
The company evaluated OpenAI models against alternatives like Gemini 2.5 Flash across conversational quality, latency, instruction following, tool calling, multilingual performance, reliability, and cost. OpenAI delivered the strongest overall balance for production workloads. In one test, GPT-5.6 Terra achieved up to 97% accuracy on common regional languages for post-call analysis, outperforming Gemini 2.5 Flash. Ringg subsequently moved summaries and sentiment classification to Terra.
"Migrating suitable real-time workloads from GPT-4.1 to GPT-5.6 reduced model costs by approximately 90% while delivering the required quality and latency," a Ringg spokesperson said. The company routes tasks to specific models: GPT-4.1 handles most real-time voice and chat traffic, GPT-5.6 Luna is used when its performance or price-performance profile fits, GPT-5.6 Terra manages post-call analysis, and GPT-5.6 Sol supports evaluation and prompt improvement.
Testing and deployment in production
Ringg's evaluation platform tests models using historical conversations and simulated customer flows before deployment. Models that pass offline testing are introduced to a small share of production traffic before broader rollout. A router monitors latency and endpoint health across regions, shifting traffic when an endpoint becomes unavailable or crosses a latency threshold. Specialized nodes, alerts, and versioned deployments help isolate problems quickly.
Customer results across healthcare and finance
Policybazaar, one of India's largest online insurance platforms, uses Ringg to connect more than 57,000 customer requests, with 67% of calls handled without human intervention. Average response time fell from 8-12 minutes to under 60 seconds. At Practo, a global healthcare platform, Ringg's agents achieved an 85% first-call resolution rate and response times below three seconds. Operating costs declined by 70% compared with the previous human-led workflow, and Ringg now completes more than 1,000 appointment bookings each day. Groww, an online investment platform, resolves 72% of inbound queries related to IPOs, futures, and options through self-service, with an average handling time of two minutes.
These results show what happens when AI agents move beyond deflection metrics to completed business outcomes. For teams exploring similar automation, AI Agent Courses cover the orchestration and evaluation approaches that make systems like this reliable in production. Customer support leaders looking to build these capabilities internally can also find targeted AI for Customer Support Courses that address integration with existing ticketing and CRM workflows.
What's next: browser agents and cross-channel context
Ringg is developing browser agents using OpenAI's computer-use capabilities for platform onboarding, Know Your Customer (KYC) processes, IT troubleshooting, on-call incident support, and claims processing. A context layer is in development to preserve information across channels, allowing customers to start a request over voice, continue on WhatsApp, and finish in a browser without repeating details.
"For Ringg, the next generation of customer operations will be measured by completed business outcomes and automation depth, rather than call volume or headcount," the spokesperson added.
Why this matters for customer support and operations leaders
The 90% cost reduction from a model migration is the kind of line item that gets CFO attention. But the operational metric that should interest support and operations leaders is the shift from deflection rates to completion rates. Ringg's customers are not just routing fewer calls to humans - they are booking appointments, resolving IPO queries, and closing insurance requests autonomously. If your team still measures success by tickets closed per agent, you are tracking the wrong number. The benchmark is moving toward business outcomes completed by the system, with headcount becoming a secondary variable.
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